We propose a Graph Neural Network (GNN)-based approach for Handwritten Mathematical Expression (HME) recognition by modeling HMEs as graphs, where nodes represent symbols and edges capture spatial dependencies. A deep BLSTM network is used for symbol segmentation, recognition, and spatial relation classification, forming an initial primitive graph. A 2D-CFG parser then generates all possible spatial relations, while the GNN-based link prediction model refines the structure by removing unnecessary connections, ultimately forming the Symbol Label Graph. Experimental results demonstrate the effectiveness of our approach, showing promising performance in HME structure recognition.

翻译：我们提出了一种基于图神经网络（GNN）的手写数学表达式（HME）识别方法，通过将HME建模为图，其中节点表示符号，边捕捉空间依赖关系。使用深度双向长短期记忆（BLSTM）网络进行符号分割、识别和空间关系分类，形成初始原始图。随后，一个二维上下文无关文法（2D-CFG）解析器生成所有可能的空间关系，而基于GNN的链接预测模型通过移除不必要的连接来优化结构，最终形成符号标签图。实验结果表明了该方法的有效性，在手写数学表达式结构识别中展现出良好的性能。